Reinforcement Learning (RL) is concerned with the solution to Markov Decision Processes (MDP). RL, due to its end-to-end nature and 0th-order oracle requirements, has been used to solve many challenging problems in game AI ([1]}, [2]}), NLP ([3]}), and robotics ([4]}). However, many of these problems correspond to the single-agent domain where the environment being stationary, is unaffected by the learning process. Often, however, an agent competes or cooperates with others, while also interacting with the common environment. Multi-Agent RL (MARL) deals with such problems and allows us to tackle more realistic decision processes. MARL has been applied to social problems like negotiation ([5]}), coordination ([6]}, [7]}), and the emergence of language ([8]}).
